Study on Short-Term Load Forecasting Method Based on the PSO and SVM model
نویسنده
چکیده
The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in order to realize the mutual supplement with each other's advantages in this paper. Firstly, the PSO algorithm is used to optimize the parameters of the SVM in order to obtain the optimal parameters of the SVM. Then a short-term load forecasting method based on combining the PSO and SVM according to the characteristics and influencing factors of short-term load forecasting is proposed. An actual power system in one region is applied to test and verify the shortterm load forecasting method. The results show that the short-term load forecasting method takes on the good convergence and higher prediction precision.
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